Deep Reinforcement Learning
Deep reinforcement learning (DRL) aims to train agents to make optimal decisions in complex environments by learning through trial and error. Current research focuses on improving DRL's robustness, sample efficiency, and interpretability, often employing architectures like Proximal Policy Optimization (PPO), deep Q-networks (DQNs), and graph neural networks (GNNs) to address challenges in diverse applications such as robotics, game playing, and resource management. The resulting advancements have significant implications for various fields, enabling the development of more adaptable and efficient autonomous systems across numerous domains.
Papers
Latency-Aware Resource Allocation for Mobile Edge Generation and Computing via Deep Reinforcement Learning
Yinyu Wu, Xuhui Zhang, Jinke Ren, Huijun Xing, Yanyan Shen, Shuguang Cui
Scenario-based Thermal Management Parametrization Through Deep Reinforcement Learning
Thomas Rudolf, Philip Muhl, Sören Hohmann, Lutz Eckstein
Adaptive Transit Signal Priority based on Deep Reinforcement Learning and Connected Vehicles in a Traffic Microsimulation Environment
Dickness Kwesiga, Angshuman Guin, Michael Hunter
Multi-agent reinforcement learning for the control of three-dimensional Rayleigh-Bénard convection
Joel Vasanth, Jean Rabault, Francisco Alcántara-Ávila, Mikael Mortensen, Ricardo Vinuesa
Image-Based Deep Reinforcement Learning with Intrinsically Motivated Stimuli: On the Execution of Complex Robotic Tasks
David Valencia, Henry Williams, Yuning Xing, Trevor Gee, Minas Liarokapis, Bruce A. MacDonald
Random Latent Exploration for Deep Reinforcement Learning
Srinath Mahankali, Zhang-Wei Hong, Ayush Sekhari, Alexander Rakhlin, Pulkit Agrawal
Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization
Carolin Benjamins, Gjorgjina Cenikj, Ana Nikolikj, Aditya Mohan, Tome Eftimov, Marius Lindauer
Deep Reinforcement Learning for Multi-Objective Optimization: Enhancing Wind Turbine Energy Generation while Mitigating Noise Emissions
Martín de Frutos, Oscar A. Marino, David Huergo, Esteban Ferrer